Plan Position Indicator Research Articles

Assessment of offshore wind conditions in coastal areas of Japan using single scanning Doppler LiDAR

Abstract In this study, offshore wind conditions in coastal areas of Japan measured by single scanning Doppler lidar is investigated. The effects of measurement range as well as rainfall and snowfall on data availability of the Doppler scanning lidar are examined. Data filtering criteria are then proposed and verified by data thinning to meet both accuracy and post-processed data availability requirements for offshore wind measurements at multiple altitudes. Finally, one-year offshore wind measurement with three different elevation angles is conducted using a single Doppler scanning lidar to investigate wind conditions in the coastal region of Northern Japan. It is found that the accuracy of 15-second average wind speed measurements by PPI (Plan Position Indicator) scan depends on the sector size. An accurate 10-minute mean wind is measured when the sector size is larger than 39 degrees and proportion of valid data acquisition is more than 10% of the time duration. The vertical and horizontal distributions of offshore wind speed in different wind directions are also analyzed and the effects of onshore topography on offshore wind conditions are clarified.

Temporal characteristics of positive cloud-to-ground flashes from tropical thunderstorms

The occurrence of positive cloud-to-ground (CG) lightning flashes proves to be a challenging task when compared to the negative CG flash which common happen in tropical region. This due to the positively charged clouds to reach higher altitudes to enable the transfer of electrical charges to the Earth's surface. With several sensors that deployed in the Malacca region (2.314077°N, 102.318282°E) such as the wide band fast and slow antenna, magnetic field sensors, and the electric field mill (EFM-100). This research study focuses on the temporal characteristics of positive CG lightning activity in the tropical region during September and October 2021. A total of 203 positive cloud-to-ground flashes were meticulously analyzed in the context of 16 thunderstorm events. Key findings highlight the consistent initiation of positive cloud-to-ground flashes through initial breakdown (IB) pulses, followed by the development of stepped leaders and subsequent return strokes. Notably, most positive cloud-to-ground events (72%) were observed as single strokes and the maximum multiplicity recorded being four subsequent return strokes. Furthermore, the analysis focuses into the temporal aspects of these lightning events, revealing average durations for various parameters, including rise-time (6.26 μs), zero-crossing (26.43 μs), pulse duration of the first RS (139.76 μs), and the interval from the first IB to the first return stroke (134.19 ms). Additionally, the recorded data from the Electric Field Mill (EFM) instruments together with Constant Altitude Plan Position Indicator (CAPPI) radar demonstrate a notable correlation. Specifically, the electric field (E-field) values exhibit a discernible increase when positive cloud-to-ground lightning events are detected near the lightning sensor.

A method of 2D and 3D display of radar terminal base on programmable pipeline

AbstractIn contrast to the ideal 3D display for meteorological radar, there is no 3D mode in the military engineering field. How to realise the 2D and 3D display for general radar terminal efficiently using the local graphics processing unit (GPU), which is impossible to realise with the traditional method of field programmable gate array now, has became an important research direction. In this paper, based on the radar echo equation, elevation was used to describe the three‐dimensional echo. After the three‐dimensional echo data model was built, the shader method with framebuffer objects was employed to display the data, while the data was capped by peripheral component interconnect express using direct memory access. It was designed to display the Plan Position Indicator and the attenuation of radar images with the 2D and 3D shaders. A series of numerical simulations were conducted to avoid image tear or lag. Compared to the methods of graphics device interface and the blend of images, the programmable pipeline of GPU performs well and is suitable for the display of radar terminal.

Comparison of line-of-sight wind speed measurements from an X-band radar and a long-range scanning lidar

As a still novel wind measurement technology, a dual-Doppler X-band wind radar system has been a substantial element of the full-scale onshore campaign AWAKEN (The American WAKE experimeNt). In order to select suitable further applications in the future and, particularly, the most optimal use cases in the wind industry for this technology, a line-of-sight wind speed verification campaign using a co-located scanning lidar as reference was set up as part of the AWAKEN campaign. The wind radar scanned in azimuth sector or plan position indicator (PPI) mode, with multiple elevations (volumetric PPI scan), while the scanning lidar remained fixed in a specific position during the verification campaign. Considering the individual spatial and temporal resolutions of the two systems, the closest points from the radar scanned volumes were compared with measurements from the scanning lidar after threshold-based and statistical data quality control. For a linear regression with 30-minute resolution data collected at 2 km range, a coefficient of determination of R 2 = 0.99 was found. Radar mean values, binned according to reference wind speed, do lie in part within the reference uncertainty but not consistently for the investigated range of line-of-sight wind speeds. Part of the reference uncertainty is hereby also associated with the procedure of comparison but kept as low as possible by optimizing the verification setup and procedure.

Simulated WSR-88D Observations of the Streamwise Vorticity Current

The streamwise vorticity current (SVC) has been shown in recent research to have a role in the tornadogenesis process in some supercells. Although field experiments have succeeded in observing the feature using mobile radars, limited knowledge exists on how/if SVCs can be observed using operational radars. To explore this possibility, simple radar emulation software was used to create simulated Weather Surveillance Radar 1988 Doppler (WSR-88D) radar images of an SVC in a high-resolution numerical simulation of a supercell thunderstorm. This proof-of-concept approach shows that SVCs likely can be observed in an operational setting under idealized circumstances. Two analysis times from the simulation are used to compare the signatures of both a weak and a strong SVC. This study suggests that results are highly contingent on radar viewing angle, and quality observations of the SVC may only be possible when a storm is within 45 km of the radar site. In Plan Position Indicator (PPI) scans, SVCs are found to be associated with inflow winds that penetrate more rearward with height downstream of the mesocyclone. Range Height Indicator (RHI) reconstructions show a reflectivity billow that resembles a density current head containing a Kelvin-Helmholtz (KH) billow, as well as a radial velocity couplet co-located with the SVC. A vertical shear product is introduced to visualize low-level vertical wind shear in the vicinity of the SVC to facilitate identification of the feature without interrogation of multiple elevation angles of radial velocity data.

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

An image processing approach to reconstruct wind using long-range wind lidars

Lidar-based wind sensing technology, originally used in the wind energy sector, is now being utilized in wind engineering to monitor wind action for designing fjord-crossing infrastructure like long-span bridges. Accurate estimation of design wind loads is crucial for the design process of such bridges. This paper examines wind data from two pairs of long-range lidars positioned along one side of the Sulafjorden, Norway, in a measurement campaign initiated by the Norwegian Public Roads Administration (NPRA). Two different scanning modes, the Plan Position Indicator (PPI) mode and the staring mode, with the fixed line-of-sight (LOS) orientation, are used to determine the line-of-sight wind speeds, the wind direction, and the related along wind speed. An image processing approach is used to compute along wind velocity information from all range gates based on the LOS wind speeds from two near-parallel lidar beams. The estimated along wind velocity is validated through wind data calculated at the intersection point of the two lidars, which provides velocity data with two horizontal components. The image-based reconstruction is found to produce reasonable wind speed estimates with a goodness-of-fit coefficient of R2 = 0.903. The mean wind direction estimate is smoothened but still comparable to the actual wind direction. The image processing approach shows promising potential to provide wind speed and direction information for all range gates for the lidar setup used, which can supplement traditional single-point wind velocity characterization by dual lidars.

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

Marine target detection for PPI images based on YOLO-SWFormer

For the task of detecting marine targets, numerous machine-learning methods have been suggested, which can achieve comparable accuracy. Despite the successful application of deep learning in the field of marine target detection in recent years, existing detection methods face challenges due to the significant interference of sea clutter. As artificial intelligence technology advances, the Swin Transformer can serve as an effective backbone for extracting discriminative features. However, it has not yet been utilized for target detection, and the combination of Swin Transformer and YOLO architecture has not been applied to similar missions. In light of this, we propose a novel method, YOLO-SWFormer, which combines the Swin Transformer and YOLO framework for target detection. Our method can extract discriminative features from plan-position indicator (PPI) images despite the interference of sea clutter, thereby reducing computational complexity and enhancing target detection accuracy. Experimental results on a Sea Clutter Database demonstrates that our method surpasses existing methods in terms of accuracy, indicating its potential as a promising solution for marine target detection tasks.

Revealing inflow and wake conditions of a 6 MW floating turbine

Abstract. We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5–9.5 and 12.5–13.5 m s−1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study.

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approaches have been used on radar echo data generated by constant altitude plan position indicator (CAPPI) radar systems for predicting cumulonimbus cloud distribution. However, the results are often too foggy and fuzzy. This paper proposes a novel approach that integrates graph convolutional networks (GCN) and trajectory gated recurrent units (TrajGRU) with an attention mechanism to predict cumulonimbus cloud distribution from radar echo data. Experiments were conducted using the moving modified National Institute of Standards and Technology (moving MNIST) dataset and real-world radar echo data, and the proposed model showed a 59.12% improvement in mean square error (MSE) and a 16.26% improvement in structure similarity index measure (SSIM) on average in the moving MNIST dataset, a 65.40% improvement in MSE, and an 10.29% improvement in SSIM on average in the radar echo dataset. These results demonstrate the effectiveness of the proposed approach for improving the prediction accuracy of cumulonimbus cloud distribution.

A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network

Accurate quantitative precipitation estimation (QPE) utilizing radar data plays a vital role in weather monitoring, hydrological modelling and prediction. In previous research, some regression techniques such as the reflectivity (Z) - rainfall (R) relationship and artificial intelligence methods were employed for radar QPE; however, the estimation results remain limited the spatial and temporal variability of rainfall results in large errors in radar QPE. Recently, an emerging deep learning approach that can be applied in hydrological areas for QPE, namely, the gated recurrent unit (GRU) network method, has been used. GRU networks have evolved from long short-term memory (LSTM) networks, with improved training speed and modelling accuracy. In this research, the GRU network is applied to QPE using the three-dimensional structure of reflectivity from the Constant Altitude Plan Position Indicator (3D CAPPI) for an urban catchment in Seoul, South Korea. Simultaneously, a “complete” 3D CAPPI based on a cubic grid of 4 × 4 km2 that is highly suitable for the study area is proposed. Based on the comparison of the results of the GRU model with those of the artificial neural networks (ANNs), recurrent neural networks (RNNs), and LSTM networks, and the Z-R relationship, namely the least-squares model (LSM) for 2 testing strategies including traditional test strategy and leave-one-out cross-validation (LOOCV) strategy, the results indicate that the GRU model is superior to other models in radar QPE, with improvements by 3.3 ∼ 46.2%, 25 ∼ 70% and 1.2 ∼ 44.8% in the root mean square error (RMSE), bias and correlation coefficient (CC). This study presents a valuable method for improving the accuracy of radar QPE that can be actively employed in the future.

Marine radar monitoring IoT system and case study of target detection based on PPI images

AbstractTo ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy.

Weather Radar Super-Resolution Reconstruction Based on Residual Attention Back-Projection Network

Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.

Preliminary Analysis of Early Identification of Three-Body Scatter Spike Signature in Hailstorms in Catalonia

Three-body scatter spike (TBSS) is one of the most common signatures associated with hailstorms using weather radar. It consists of an appendage appearing behind the storms in the radar imagery. The cause is the multi-scattering between hail particles and the ground. The TBSS is detected in plan position indicator (PPI) reflectivity fields at the elevation coinciding with the hail core. Surveillance staff usually analyzes low-level PPI in real-time, but the signature can appear at mid or high elevations. This study takes advantage of the three-dimensional maximum reflectivity (MAX) product for detecting the presence of the TBSS in thunderstorms more easily, instead of the usual way of analyzing the PPI fields. The new technique is more direct and immediate, crucial in real-time surveillance tasks. This preliminary analysis has focused on the hail days with ground registers that occurred in Catalonia between January and August of 2022, observing this signature in the MAX product in 96% of days with large hail and 72% of small hail events. This fact indicates that MAX can be useful for identifying large hail in thunderstorms, but the efficiency decreases for hail smaller than 2 cm.

Correlation analysis between lightning flashes and rainfall rate during a flash flood thunderstorm

This paper presents the correlation analysis between lightning flashes and the rainfall rate of a thunderstorm when a flash flood event happened in Melaka on 11 August 2020. Four types of data have been collected from the electric field mill (EFM), fast antenna (FA) system, constant altitude plan position indicator (CAPPI) radar and world wide lightning location network (WWLLN). Two storms have been detected by the EFM occurred between 04:00:00 and 14:00:00. The FA system recorded a total of 33 lightning flashes had detected with the highest number occurrence of flashes which positive narrow bipolar event (+NBE) around 21 flashes, the maximum rainfall rate and reflectivity have been detected by radar during the first storm were 8 mm h-1 and 37 dBZ, respectively (light rain). During the second storm, there was a total of 980 lightning flashes detected by the FA system with the highest number occurrence of flashes around 429 flashes (IC), the highest value of rainfall rate and reflectivity is 50 mm h-1 and 50 dBZ, respectively (heavy rain). Analyses of the lightning and rainfall rate data also show a close link between the occurrence of major thunderstorms systems and flash flooding on a regional scale.

Complementarity of wind measurements from co-located X-band weather radar and Doppler lidar

Abstract. Accurate wind profile measurements are important for applications ranging from aviation to numerical weather prediction. The spatial pattern of winds can be obtained with ground-based remote sensing instruments, such as weather radars and Doppler lidars. As the return signal in weather radars is mostly due to hydrometeors or insects, and in Doppler lidars due to aerosols, the instruments provide wind measurements in different weather conditions. However, the effect of various weather conditions on the measurement capabilities of these instruments has not been previously extensively quantified. Here we present results from a 7-month measurement campaign that took place in Vantaa, Finland, where a co-located Vaisala WRS400 X-band weather radar and WindCube 400S Doppler lidar were employed continuously to perform wind measurements. Both instruments measured plan position indicator (PPI) scans at 2.0∘ elevation from the horizontal. Direct comparison of radial Doppler velocities from both instruments showed good agreement with R2=0.96. We then examined the effect of horizontal visibility, cloud base height, and precipitation intensity on the measurement availability of each instrument. The Doppler lidar displayed good availability in clear air situations and the X-band radar in precipitation. Both instruments exhibited high availability in clear air conditions in summer when insects were present. The complementary performance in the measurement availability of the two instruments means that their combination substantially increases the spatial coverage of wind observations across a wide range of weather conditions.

Analysis of Pre-Monsoon Convective Systems over a Tropical Coastal Region Using C-Band Polarimetric Radar, Satellite and Numerical Simulation

Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have much higher spread over convective regions. The distribution of Kdp is almost uniform across the vertical over the stratiform regions. The mean profile of Zdr over stratiform regions shows a distinct local maxima near melting level. A comprehensive analysis has been done on an isolated deep convective system on 13 May 2018. Plan position indicator (PPI) diagrams and satellite measured cloud top temperature demonstrate that pre-monsoon deep convective systems can develop very rapidly within a very short span of time over the region. Heavy precipitation near the surface is reflected in the high value of Kdp (>5° km−1). High values of Zdr (>3 dB) were measured at lower levels indicating the oblate shape of bigger raindrops. A fuzzy logic-based hydrometeor identification algorithm has been applied with five variables (Zh, Zdr, ρhv, Kdp, and T) to understand the bulk microphysical properties at different heights within the storm. The presence of bigger graupel particles near the melting layer indicates strong updrafts within the convective core regions. The vertical ice hydrometeor signifies the existence of a strong electric field causing them to align vertically. Numerical simulation with the spectral bin microphysics (SBM) scheme could produce most of the features of the storm reasonably well. In particular, the simulated reflectivity, graupel mixing ratio and rainfall were in good agreement with the observed values.

Analysis of the Characteristics and Evolution Mechanisms of a Bow-Shaped Squall Line in East China Observed with Dual-Polarization Doppler Radars

To gain a deeper understanding of the formation and evolutionary mechanisms of a bow-shaped squall line (BSL) that occurred in East China on 10 May 2021, observations from S-band dual-polarization radars, a disdrometer and other instruments are used to investigate the characteristics and evolution of the kinematic, microphysical and radar echo structure within the squall line during its formative and mature stages. The results are as follows. The updraft induced by upper-level divergence and vertical thermal instability induced by the cold source at the middle and top of the troposphere provided environmental conditions suitable for the formation and strengthening of a squall line. The characteristics of the vertical vorticity at the leading edge of the squall line provided a good indication of its echo structure and evolutionary trend. The mechanism behind a new echo phenomenon—double high-differential reflectivity (ZDR) bands—observed in plan position indicator scans produced by the dual-polarization radar is investigated from the kinematic and microphysical structural perspectives. The evolutionary characteristics of the microphysical structure of the bulk of the squall line and its trailing stratiform cloud region are analyzed based on the quasi-vertical profiles retrieved from the S-band dual-polarization radar in Quzhou. Moreover, a conceptual model describing this type of BSL with a trailing region of stratiform rain in the warm sector is developed to provide technical support for the monitoring and early warning of BSLs.

Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.